Random Forests for Time Series
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.57805/revstat.v21i2.400 |
Resumo: | Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to replace standard bootstrap with a dependent block bootstrap to subsample time series during tree construction. We present numerical experiments on electricity load forecasting. The first, at a disaggregated level and the second at a national level focusing on atypical periods. For both, we explore a heuristic for the choice of the block size. Additional experiments with generic time series data are also available. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Random Forests for Time SeriesBlock bootstrapRandom forestsRegressionTime seriesRandom forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to replace standard bootstrap with a dependent block bootstrap to subsample time series during tree construction. We present numerical experiments on electricity load forecasting. The first, at a disaggregated level and the second at a national level focusing on atypical periods. For both, we explore a heuristic for the choice of the block size. Additional experiments with generic time series data are also available.Statistics Portugal2023-06-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v21i2.400https://doi.org/10.57805/revstat.v21i2.400REVSTAT-Statistical Journal; Vol. 21 No. 2 (2023): REVSTAT-Statistical Journal; 283–302REVSTAT; Vol. 21 N.º 2 (2023): REVSTAT-Statistical Journal; 283–3022183-03711645-6726reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://revstat.ine.pt/index.php/REVSTAT/article/view/400https://revstat.ine.pt/index.php/REVSTAT/article/view/400/643Copyright (c) 2021 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessGoehry, BenjaminYan , HuiGoude , YannigMassart , PascalPoggi , Jean-Michel2023-07-01T06:30:12Zoai:revstat:article/400Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:02:13.234448Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Random Forests for Time Series |
title |
Random Forests for Time Series |
spellingShingle |
Random Forests for Time Series Goehry, Benjamin Block bootstrap Random forests Regression Time series |
title_short |
Random Forests for Time Series |
title_full |
Random Forests for Time Series |
title_fullStr |
Random Forests for Time Series |
title_full_unstemmed |
Random Forests for Time Series |
title_sort |
Random Forests for Time Series |
author |
Goehry, Benjamin |
author_facet |
Goehry, Benjamin Yan , Hui Goude , Yannig Massart , Pascal Poggi , Jean-Michel |
author_role |
author |
author2 |
Yan , Hui Goude , Yannig Massart , Pascal Poggi , Jean-Michel |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Goehry, Benjamin Yan , Hui Goude , Yannig Massart , Pascal Poggi , Jean-Michel |
dc.subject.por.fl_str_mv |
Block bootstrap Random forests Regression Time series |
topic |
Block bootstrap Random forests Regression Time series |
description |
Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to replace standard bootstrap with a dependent block bootstrap to subsample time series during tree construction. We present numerical experiments on electricity load forecasting. The first, at a disaggregated level and the second at a national level focusing on atypical periods. For both, we explore a heuristic for the choice of the block size. Additional experiments with generic time series data are also available. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06-26 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.57805/revstat.v21i2.400 https://doi.org/10.57805/revstat.v21i2.400 |
url |
https://doi.org/10.57805/revstat.v21i2.400 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revstat.ine.pt/index.php/REVSTAT/article/view/400 https://revstat.ine.pt/index.php/REVSTAT/article/view/400/643 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 REVSTAT-Statistical Journal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 REVSTAT-Statistical Journal |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Statistics Portugal |
publisher.none.fl_str_mv |
Statistics Portugal |
dc.source.none.fl_str_mv |
REVSTAT-Statistical Journal; Vol. 21 No. 2 (2023): REVSTAT-Statistical Journal; 283–302 REVSTAT; Vol. 21 N.º 2 (2023): REVSTAT-Statistical Journal; 283–302 2183-0371 1645-6726 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131688423391232 |